scholarly journals Combining Graph Clustering and Quantitative Association Rules for Knowledge Discovery in Geochemical Data Problem

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 40453-40473
Author(s):  
Yasmina Medjadba ◽  
Dan Hu ◽  
Wei Liu ◽  
Xianchuan Yu
Keyword(s):  
Knowledge Discovery ◽  
Association Rules ◽  
Graph Clustering ◽  
Geochemical Data ◽  
Quantitative Association Rules ◽  
Data Problem

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

MRQAR: A generic MapReduce framework to discover quantitative association rules in big data problems

2018 ◽  
Vol 153 ◽  
pp. 176-192 ◽  
Author(s):  
D. Martín ◽  
M. Martínez-Ballesteros ◽  
D. García-Gil ◽  
J. Alcalá-Fdez ◽  
F. Herrera ◽  
...  
Keyword(s):  
Big Data ◽  
Association Rules ◽  
Mapreduce Framework ◽  
Quantitative Association Rules

Enhancing the Process of Knowledge Discovery in Geographic Databases Using Geo-Ontologies

2009 ◽  
pp. 2405-2426 ◽  
Author(s):  
Vania Bogorny ◽  
Paulo Martins Engel ◽  
Luis Otavio Alavares
Keyword(s):  
Knowledge Discovery ◽  
Prior Knowledge ◽  
Association Rules ◽  
Pattern Mining ◽  
Spatial Association ◽  
Geographic Pattern ◽  
Geographic Patterns ◽  
Geographic Databases ◽  
Novel Approach ◽  
Amount Of Knowledge

This chapter introduces the problem of mining frequent geographic patterns and spatial association rules from geographic databases. In the geographic domain most discovered patterns are trivial, non-novel, and noninteresting, which simply represent natural geographic associations intrinsic to geographic data. A large amount of natural geographic associations are explicitly represented in geographic database schemas and geo-ontologies, which have not been used so far in frequent geographic pattern mining. Therefore, this chapter presents a novel approach to extract patterns from geographic databases using geoontologies as prior knowledge. The main goal of this chapter is to show how the large amount of knowledge represented in geo-ontologies can be used to avoid the extraction of patterns that are previously known as noninteresting.

A novel hybrid GA–PSO framework for mining quantitative association rules

2019 ◽  
Vol 24 (6) ◽  
pp. 4645-4666 ◽  
Author(s):  
Fateme Moslehi ◽  
Abdorrahman Haeri ◽  
Francisco Martínez-Álvarez
Keyword(s):  
Association Rules ◽  
Quantitative Association Rules
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